This study aimed to develop a model for accurately predicting the acute aquatic toxicity (48h- EC50) of chlorine disinfection by-products (DBPs). DBPs have caused environmental risks, but experimental toxicity data are difficult to obtain due to time, cost, and ethical constraints. Therefore, a deep learning model was developed using actual concentration-based data. Toxicity data for 139 aliphatic chlorinated compounds were from the OECD QSAR Toolbox and from aquatic toxicity test results provided by the japan ministry of the environment. Various concentration criteria, including nominal and measured concentrations, were encoded as additional inputs, and EC50 values were augmented via log transformation and structural string modifications to overcome small data limitations. The directed message passing neural network (D-MPNN) model, which considers bond directionality, was applied to reflect structural complexity accurately. Also, this model effectively reflected subtle structural differences and showed stable performance even with limited data. Comparisons between models with and without concentration criteria revealed that the model considering all concentration criteria had superior predictive accuracy. This result shows that concentration criteria are a critical factor in toxicity prediction. This study suggests a baseline model that works reliably even with small datasets reflecting realistic concentration criteria, showing its potential use for replacing some experiments and for screening toxic substances.